• Sarang Joshi
• Jaykumar Girase
• Anish Bhujbal
• Milind Gajare

Pathfinding is a fundamental challenge in robotics, particularly for autonomous navigation in structured and unstructured environments. This paper presents a comparative analysis of five widely used pathfinding algorithms: Flood Fill, A*, Dijkstra’s Algorithm, Greedy Best-First Search (GBFS), and Wall Following, implemented on an ATmega328P-based autonomous robot to navigate mazes of varying complexities. The study evaluates each algorithm based on execution time, memory consumption, and scalability across different maze sizes (7×7, 9×9, 11×11, and 13×13). The performance metrics are analyzed to determine the trade-offs between computation time, optimal path efficiency, and resource utilization. The findings reveal that informed search algorithms such as A* and Dijkstra's Algorithm outperform others in terms of path optimality and reliability, while uninformed search methods like Flood Fill and BFS ensure completeness but at the cost of increased computational overhead. The Wall-Following algorithm, though simplistic, demonstrates guaranteed path discovery in connected mazes but lacks efficiency in minimizing traversal length. The experimental results provide insight into the suitability of these algorithms for real-world applications, emphasizing their applicability in constrained environments such as robotics, artificial intelligence, and emergency response systems.

Automation, navigation, Heuristic algorithm, Path planning, Energy efficient

"Comparative Performance Analysis of Pathfinding Algorithms for Autonomous Navigation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 6, page no.d900-d907, June-2025, Available :http://www.jetir.org/papers/JETIR2506400.pdf

"Comparative Performance Analysis of Pathfinding Algorithms for Autonomous Navigation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 6, page no. ppd900-d907, June-2025, Available at : http://www.jetir.org/papers/JETIR2506400.pdf